The present invention relates to a method and a device for automatic evaluation of cereal kernels or grains and similar granular products, e.g. beans, rice and seeds, which are handled in bulk.
Each shipment of cereals may contain a certain amount of kernels of some other kind of cereal than the desired one, for example rye and wild oats in shipments of wheat, and of kernels which per se are of the desired kind but which are of unsatisfactory quality, for example broken-off kernels, kernels chewed by animals, green kernels and burnt kernels. Also stones and other objects are to be found among the kernels.
Since the payment for cereal kernels is based on purity and quality, it is important that these parameters can be evaluated correctly. Today, the evaluation is carried out manually by visual inspection of samples from cereal shipments, and weighing of the different amounts of kernels of incorrect kinds and of kernels of the correct kind but of unsatisfactory quality. It is instead desirable to be able to make this evaluation automatically.
When exporting and importing cereals, there is a need of being able to quickly characterise the cereals for purity, homogeneity and evenness in colour. Today there is no equipment for effecting this automatically.
In the handling of cereals in, among other countries, the U.S.A., Canada and Australia, staff is available to evaluate the composition of the supplied cereals to determine the suitable future use (pasta products, bread, feed etc.). Since this evaluation can be made by experienced staff only, it would be a great advantage if, instead, it could be effected automatically.
In the handling of cereals it is also important that the size of the kernels can be evaluated. This is now carried out by letting the kernels pass a number of sieves having a gradually diminishing width of mesh. It is desirable that the size evaluation can be carried out in a more rational manner.
Scientific literature comprises examples of experiments being made to evaluate cereals by means of computerised image analysis. An article by Sapirstein et al. in Cereal Science No. 6, 1987, p. 3, describes an experiment of classifying wheat, rye, barley and oat kernels by means of different contour parameters. By analysing a statistically calculated combination of length/width, width, moment and length of circumference, an image analysing program could identify kernels with an accuracy of more than 97%.
An article by Zayas et al. in Cereal Chemistry 66(3), 1989, p. 233, describes an image analysing system for determining "non-wheat components" in samples of wheat. The shape of the wheat kernels is described by means of 10 geometrical parameters, and furthermore a measure of the colour of the wheat kernels is used, expressed in grey scale.
The above-mentioned systems are, however, not commercially applicable since they are experimental and based on the fact that the kernels are presented manually one by one to the image analysing systems.
In patent literature there are also examples of systems for automatic evaluation of cereal kernels and other objects.
GB 2,012,948 discloses a method of determining the distribution of sizes for samples of, inter alia, cereal kernels. According to this method, the kernels are caused to fall between a screen which is illuminated by a stroboscope, and a video camera by means of which images of the kernels are produced. The video images are digitised and the kernels are identified in the images. Based on the size of each image of a kernel, the distribution of sizes of the kernels in the sample is determined. By this method, it is, however, not possible to classify the kernels. Moreover, it is not possible to determine the size of all kernels.
WO 91/17525 discloses a method for automatically classifying an object into predetermined classes. According to this method, a video camera takes time-domain images of objects which are carried one by one on a conveyor belt past the camera. The time-domain images are transformed by Fourier analysis into frequency-domain signals which form input signals to a neural network effecting the actual classification. By this method, it is not possible to analyse a sufficient amount of objects per unit of time to make the method commercially useful for classification of cereals.